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> I feel like 2 years ago the self-driving car manufacturers were releasing crazy deadlines, like full self-driving in 5 years. Everything is always five years

> I feel like 2 years ago the self-driving car manufacturers were releasing crazy deadlines, like full self-driving in 5 years. Everything is always five years away. That's the sweet spot for getting lots of funding while allowing time for the wow factor to wear off so you can backtrack on what you really intend to deliver (if anything at all).

dmix 7y ago
This isn’t unique to car AI, every software project looks doable at the start, you can even get “90%” there where it looks and feels like a real solution. But then real life usage testing starts or the implementation process exposes a thousand small problems you didn’t except in the last 10%, or your own original design wasn’t flexible enough for some constraints requiring a significant rethinking of that 90%. Just ask Jeff Hawkins. This is going to be a huge business opportunity for car manufacturers and software companies, two industries with access to plenty of capital. So the not being left behind risk (FOMO) is naturally going to be high. Combine that with potentially saving hundreds of thousands of lives from car accidents and being able to look at your phone instead of driving (freeing up countless human hours) and you have the recipe for extreme optimism. The only important question is whether x company overpaid in the short term for acquisitions or scaling up ... and/or lack a runway to really achieve their goals. Otherwise it’s pretty par for the course, especially for software/innovation at this scale. It’s quite possible we’ll hit these goals sooner now that industry went full bore on solving these problems and we’ll benefit as a society in many ways besides transportation from the AI work. Most importantly, who cares if some pension funds and wealthy investors involved in high risk capital investment lose some money in the short term.
- irq11 7y ago
  ”This isn’t unique to cars, every software project looks easy at the start, you can even get “90%” there where it looks and feels like a real solution. But then real life usage testing starts or the implementation process exposes a thousand small problems you didn’t except in the last 10%, or your own original design wasn’t flexible enough for some constraints requiring a significant rethinking of that 90%.” Yep, and in silicon valley, anyone who seriously plans for the consequences of this is dismissed as insufficiently futurist. It’s hard to thrive as a responsible engineer when there’s always another huckster around the corner, willing to promise that 5-year future today.
- crooked-v 7y ago
  It's like they say: the first 90% of the work takes 90% of the time, and the last 10% of the work takes the other 90% of the time.
foobiekr 7y ago
Deep learning as an approach has the specific issue that already overconfident people can make shockingly fast progress and get to an 85% solution quickly by investing a lot in low-difficulty labor like data set curation. This lends a crazy amount of misplaced confidence to the people involved as they start trying to get to the last few %. Most software people have instincts that are all wrong on this because it’s the opposite of the usual experience.

dmix 7y ago

This isn’t unique to car AI, every software project looks doable at the start, you can even get “90%” there where it looks and feels like a real solution. But then real life usage testing starts or the implementation process exposes a thousand small problems you didn’t except in the last 10%, or your own original design wasn’t flexible enough for some constraints requiring a significant rethinking of that 90%. Just ask Jeff Hawkins. This is going to be a huge business opportunity for car manufacturers and software companies, two industries with access to plenty of capital. So the not being left behind risk (FOMO) is naturally going to be high. Combine that with potentially saving hundreds of thousands of lives from car accidents and being able to look at your phone instead of driving (freeing up countless human hours) and you have the recipe for extreme optimism. The only important question is whether x company overpaid in the short term for acquisitions or scaling up ... and/or lack a runway to really achieve their goals. Otherwise it’s pretty par for the course, especially for software/innovation at this scale. It’s quite possible we’ll hit these goals sooner now that industry went full bore on solving these problems and we’ll benefit as a society in many ways besides transportation from the AI work. Most importantly, who cares if some pension funds and wealthy investors involved in high risk capital investment lose some money in the short term.

irq11 7y ago
”This isn’t unique to cars, every software project looks easy at the start, you can even get “90%” there where it looks and feels like a real solution. But then real life usage testing starts or the implementation process exposes a thousand small problems you didn’t except in the last 10%, or your own original design wasn’t flexible enough for some constraints requiring a significant rethinking of that 90%.” Yep, and in silicon valley, anyone who seriously plans for the consequences of this is dismissed as insufficiently futurist. It’s hard to thrive as a responsible engineer when there’s always another huckster around the corner, willing to promise that 5-year future today.
crooked-v 7y ago
It's like they say: the first 90% of the work takes 90% of the time, and the last 10% of the work takes the other 90% of the time.

foobiekr 7y ago

Deep learning as an approach has the specific issue that already overconfident people can make shockingly fast progress and get to an 85% solution quickly by investing a lot in low-difficulty labor like data set curation. This lends a crazy amount of misplaced confidence to the people involved as they start trying to get to the last few %. Most software people have instincts that are all wrong on this because it’s the opposite of the usual experience.